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局部读取标签化可实现长读长小型变异calling 的准确性。

Local read haplotagging enables accurate long-read small variant calling.

机构信息

Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA, USA.

UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.

出版信息

Nat Commun. 2024 Jul 13;15(1):5907. doi: 10.1038/s41467-024-50079-5.

Abstract

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.

摘要

长读测序技术能够检测基因组中难以映射的区域的变异,并在临床环境中实现快速基因诊断。Pacific Biosciences(PacBio)和 Oxford Nanopore Technologies(ONT)等快速发展的第三代测序平台正在引入更新的平台和数据类型。已经证明,基于深度神经网络的变异调用方法可以使用长读序列的局部单倍型信息来提高基因分型准确性。然而,使用局部单倍型信息会产生开销,因为需要多次执行变异调用,这最终使得难以扩展到新的数据类型和平台,因为它们会不断引入。在这项工作中,我们开发了一种局部单倍型近似方法,该方法能够在包括 PacBio Revio 系统、ONT R10.4 单链和双链数据在内的多个测序平台上实现最先进的变异调用性能。这种局部单倍型近似的添加简化了 DeepVariant 的长读变异调用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21e0/11246426/e860b6f03f8b/41467_2024_50079_Fig1_HTML.jpg

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